A statistical categorization-based curriculum learning approach for multi-task classification of images

Image classification and the detection of features within images remain significant challenges in computer vision. Several approaches, including serial task models and multi-output models, have been explored to address these challenges. This study focuses on multitasking attention mechanisms, which enable simultaneous categorization of data and tasks. By applying a statistical framework, the proposed method enhances the efficiency and accuracy of image classification and feature detection, with a focus on handling multiple tasks concurrently. To enhance the robustness of the model, a data-driven approach based on curriculum learning was proposed. The experiments were conducted using two distinct datasets. The first dataset involves forensic examinations, specifically identifying firearms and their calibers from firing pin marks. The proposed model achieved an accuracy of 95% in brand detection and 98% in caliber detection on this dataset. In the second part of the experiments, the animals with attributes 2 (AwA2) dataset, where state-of-the-art models have previously been applied, was used. The proposed model reduced classification errors by 1 to 10% compared to traditional convolutional neural network (CNN) architectures. The experimental results from both the forensic and public datasets demonstrate that the proposed model effectively handles multitask classification tasks, validating its applicability across diverse domains.